MPPT control of photovoltaic cells based on dynamic recurrent fuzzy neural network

被引：0
|
作者：
Sun, Yuxin [1 ]
Zhang, Jing [1 ]
Liu, Xianxing [1 ]
机构：
[1] Univ Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China
来源：
ADVANCED MATERIALS AND PROCESS TECHNOLOGY, PTS 1-3 | 2012年 / 217-219卷
关键词：
photovoltaic cells; maximum power point tracking(MPPT); dynamic recurrent fuzzy neural network(DRFNN);
D O I：
10.4028/www.scientific.net/AMM.217-219.805
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The output characteristics of photovoltaic cells varies with the load and the external environment. In order to improve the conversion efficiency of PV cells, it is necessary to keep the system working in maximum power point(MPP) nearby. According to maximum power point tracking for PV systems,a new intelligent control method based on dynamic recurrent fuzzy neural network is proposed. The simulation results show that when the external environment changes,the system can make rapid response and accurately track the maximum power point of PV cells. What's more,it has good control accuracy and stability.
引用
收藏
页码：805 / 808
页数：4
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